Classification and recognition algorithms have been developed to detect specific or distinct signatures of targets within an array of data. The detection capability of the classification/recognition algorithms may be limited by variations in environmental conditions, sensor settings, scene complexity and degree of clutter. Individual classification/recognition algorithms may be combined to form an ensemble classifier. The ensemble classifier may have improved detection performance relative to the detection performance of an individual classification/recognition algorithm. The improved performance for detecting regions of interest such as, for example, military targets, or malicious cells/lesions or patterns, depends upon the selection of algorithms for use in the ensemble classifier.
However, a detection system may include a sensor that receives analysis data, a machine readable medium communicably coupled to the sensor, an ensemble classifier stored on the machine readable medium, and a processor communicably coupled to the sensor. The ensemble classifier includes parent recognition algorithms having a predicted performance of a low dependency, such that the predicted performance is a function of a minimum double-fault measure of a recognition algorithm. The processor executes the ensemble classifier to detect a target within the analysis data.
Accordingly, a need exists for alternative methods and logic for and systems for generating an ensemble classifier, and systems comprising an ensemble classifier.